autosearch

AutoSearch

Research that gets smarter every time you use it.
34 search channels. Self-evolving queries. Cited reports. Zero API keys.

Website • Documentation • Quick Start • What Makes It Different • Self-Evolution • How It Works • Channels • Contributing

Quick Start

npm install -g @0xmariowu/autosearch

Then start a new Claude Code session and run:

/autosearch "compare vector databases for RAG applications"

curl -fsSL https://raw.githubusercontent.com/0xmariowu/autosearch/main/scripts/install.sh | bash

AutoSearch asks two questions — how deep and what format — then searches, evaluates, and delivers a cited report with real-time progress:

[Phase 1/6] Recall     — 25 rubrics, 47 items recalled, 15 queries planned
[Phase 2/6] Search     — 62 results from 12 channels
[Phase 3/6] Evaluate   — 54 relevant, 3 gap queries
[Phase 4/6] Synthesize — report ready (38 citations)
[Phase 5/6] Rubrics    — 23/25 rubrics passed
[Phase 6/6] Evolve     — 4 patterns saved

What Makes It Different

Self-Evolution

This is the core idea. Most search tools run the same strategy every time. AutoSearch learns from every session and gets measurably better.

How it works: after each search, AutoSearch records which queries found relevant results and which returned nothing. Next time, it skips what failed and doubles down on what worked. Over sessions, it builds a profile of which channels are useful for which types of topics.

What it looks like in practice:

Session 1: "vector databases for RAG"
  → Searched 15 channels, 8 had results
  → Learned: arxiv + github-repos are high-yield for this topic
  → Learned: producthunt and crunchbase returned nothing useful
  → Saved 3 winning query patterns

Session 2: same topic, 2 weeks later
  → Auto-skipped channels that failed last time
  → Reused winning query patterns, added freshness filter
  → Found 12 new results the first session missed
  → Score improved: 0.65 → 0.78

Session 3: different topic ("AI agent frameworks")
  → Applied cross-topic patterns (arxiv query structure, github star filter)
  → Reached 0.71 on first attempt (vs 0.58 baseline)

The safety mechanism: the evaluator (judge.py) is fixed and cannot be modified by evolution. Only search strategy evolves — not the scoring. This prevents the system from gaming its own metrics.

How It Works

You: /autosearch "topic"
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[1] Claude recalls what it already knows → maps 9 knowledge dimensions
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[2] Identifies gaps → generates queries ONLY for what Claude doesn't know
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[3] Searches 34 channels in parallel (10-30 seconds)
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[4] LLM evaluates each result for relevance, filters noise
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[5] Synthesizes report with two-stage citation lock
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[6] Checks quality rubrics → evolves strategy → commits improvements